Design Modification Implementations for Mitigating the Reactor Inner Zone Inlet Header Temperature in CANDU Reactor Units
Bibliographic record
Abstract
Abstract The Reactor Inner Zone Inlet Header (RIZIH) temperatures have raised more rapidly in the CANDU units in general, compared to the original aging predictions. This adverse trend is caused by a degradation mechanism that affecting heat exchange efficiency in certain areas of the process systems. The main contributor to the RIZIH temperature increase is the fouling in the preheaters and steam generator/boilers due to magnetite deposits on tube internal diameters. The RIZIH temperature rise had caused units de-rates to ensure the reactor safety and comply with the regulatory requirements. As reported in a previous PVP paper (PVP2017-65096), multiple design alternatives were considered and evaluated to address the adverse condition, the best design option with a piping modification by adding external feedwater bypass of high pressure heater was selected to improve RIZIH temperature control. Following the conceptual engineering, preliminary design and detail design, the engineering change was implemented in two CANDU reactor units between 2018 and 2019. This paper reports out the field physical implementations and discusses the effectiveness of the design change on mitigating the RIZIH temperature rise, it also presents the operational and financial benefits actualized through observations of the two implementing units.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".